
# Import seaborn
import seaborn as sns
import pandas as pd

# Apply the default theme
sns.set_theme()

# Load an example dataset
# tips = sns.load_dataset("tips")

df = pd.read_csv("./data/tips.csv")
if df.iloc[-1].isnull().all():
    df = df.iloc[:-1]

df["day"] = pd.Categorical(df["day"], ["Thur", "Fri", "Sat", "Sun"])
df["sex"] = pd.Categorical(df["sex"], ["Male", "Female"])
df["time"] = pd.Categorical(df["time"], ["Lunch", "Dinner"])
df["smoker"] = pd.Categorical(df["smoker"], ["Yes", "No"])


# Create a visualization
sns.relplot(
    data=df,
    x="total_bill", y="tip", col="time",
    hue="smoker", style="smoker", size="size",
)

<seaborn.axisgrid.FacetGrid at 0x110b8fca0>


sns.displot(data=df, x="total_bill", col="time", kde=True)

<seaborn.axisgrid.FacetGrid at 0x1262772e0>


df = pd.read_csv("./data/penguins.csv")
if df.iloc[-1].isnull().all():
    df = df.iloc[:-1]
df["sex"] = df["sex"].str.title()
sns.jointplot(data=df, x="flipper_length_mm", y="bill_length_mm", hue="species")

<seaborn.axisgrid.JointGrid at 0x1260728b0>


sns.pairplot(data=df, hue="species")

<seaborn.axisgrid.PairGrid at 0x126a007c0>


import ipyvolume
ds = ipyvolume.datasets.aquariusA2.fetch()
ipyvolume.quickvolshow(ds.data, lighting=True)

/Users/wangyufeng/opt/anaconda3/lib/python3.8/site-packages/ipyvolume/serialize.py:92: RuntimeWarning: invalid value encountered in true_divide
  gradient = gradient / np.sqrt(gradient[0] ** 2 + gradient[1] ** 2 + gradient[2] ** 2)


from bokeh.plotting import figure, output_notebook, show
output_notebook() # needs to be called to output in the notebook

# prepare some data
x = [0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0]
y = [i**2 for i in x]

# create a new plot
fig = figure(
    tools="pan,box_zoom,wheel_zoom,zoom_in,zoom_out,reset,save",
    title="Example Bokeh plot",
    y_axis_type="log",
    y_range=[0.001, 10**3],
    x_axis_label='Sections',
    y_axis_label='Particles (log)',
    plot_width=600, plot_height=400,
)

# plot some data ('renderers' in Bokeh)
fig.circle(x, x, legend_label="y=x", fill_color="white", size=8)
fig.line(x, y, legend_label="y=x^2", line_width=3, line_color="red")


# show the results
show(fig)


import plotly.graph_objects as go

# prepare some data
x = [0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0]
y = [i**2 for i in x]

# create a new plot
fig = go.Figure(
    layout=dict(
        title="Example Plotly plot",
        yaxis_type="log",
        yaxis_range=[-3, 3],  # Plotly takes ranges differently!
        xaxis_title='sections',
        yaxis_title='particles',
    )
)


# show the results
# plot some data ('traces' in Plotly)
fig.add_trace(go.Scatter(x=x, y=x, mode='markers', name="y=x", marker=dict(color='royalblue', size=8)))
fig.add_trace(go.Scatter(x=x, y=y, name="y=x^2", line=dict(width=3)))
fig.show()


import pyvista as pv
from pyvista import examples
import numpy as np
import rasterio

topo = pv.read('./fig/topo_clean.vtk')
filename = './fig/Geologic_map_on_air_photo.tif'
texture = pv.read_texture(filename)

def get_gcps(filename):
    """This helper function retrieves the Ground Control
    Points of a GeoTIFF. Note that this requires gdal"""
    get_point = lambda gcp: np.array([gcp.x, gcp.y, gcp.z])
    # Load a raster
    src = rasterio.open(filename)
    # Grab the Groung Control Points
    points = np.array([get_point(gcp) for gcp in src.gcps[0]])
    # Now Grab the three corners of their bounding box
    # -- This guarantees we grab the right points
    bounds = pv.PolyData(points).bounds
    origin = [bounds[0], bounds[2], bounds[4]]  # BOTTOM LEFT CORNER
    point_u = [bounds[1], bounds[2], bounds[4]]  # BOTTOM RIGHT CORNER
    point_v = [bounds[0], bounds[3], bounds[4]]  # TOP LEFT CORNER
    return origin, point_u, point_v


# Fetch the GCPs
origin, point_u, point_v = get_gcps(filename)
# Use the GCPs to map the texture coordiantes onto the topography surface
topo.texture_map_to_plane(origin, point_u, point_v, inplace=True)

# Show GCPs in relation to topo surface with texture coordinates displayed
p = pv.Plotter()
p.add_point_labels(
    np.array(
        [
            origin,
            point_u,
            point_v,
        ]
    ),
    ["Origin", "Point U", "Point V"],
    point_size=5,
)

p.add_mesh(topo)

# Now plot the topo surface with the texture draped over it
# And make window size large for a high-res screenshot
p = pv.Plotter(window_size=np.array([1024, 768]) * 3)
p.add_mesh(topo, texture=texture)
p.camera_position = [
    (337461.4124956896, 4257141.430658634, 2738.4956020899253),
    (339000.40935731295, 4260394.940646875, 1724.0720826501868),
    (0.10526647627366331, 0.2502863297360612, 0.962432190920575),
]


p.show()


from IPython.display import HTML
HTML('<iframe src="https://player.vimeo.com/video/459406913?h=1613ccc704" width="640" height="426" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen></iframe><p><a href="https://vimeo.com/459406913">Earth&#039;s mantle convection</a> from <a href="https://vimeo.com/computecanada">Compute Canada</a> on <a href="https://vimeo.com">Vimeo</a>.</p>')

地学数据三维可视化-工具¶

信息可视化和科学可视化¶

信息可视化和科学可视化¶

Matplotlib¶

信息可视化工具-Matplotlib¶

信息可视化工具-Javascript¶

信息可视化工具-ipywidgets¶

信息可视化工具-Bokeh¶

信息可视化工具-Plotly¶

Bokeh vs. Plotly¶

科学可视化工具-Kitware¶

科学可视化工具-Enthought¶

科学可视化工具-OpenGL¶

科学可视化工具-VTK¶

科学可视化工具¶

Communities¶

Conferences¶

Conferences¶

Companies¶

下节课预告¶